@article{RN294,
   author = {Kaneko, H. and Endo, H. and Henry, N. and Berney, C. and Mahe, F. and Poulain, J. and Labadie, K. and Beluche, O. and El Hourany, R. and Tara Oceans, Coordinators and Chaffron, S. and Wincker, P. and Nakamura, R. and Karp-Boss, L. and Boss, E. and Bowler, C. and de Vargas, C. and Tomii, K. and Ogata, H.},
   title = {Predicting global distributions of eukaryotic plankton communities from satellite data},
   journal = {ISME Commun},
   volume = {3},
   number = {1},
   pages = {101},
   note = {Kaneko, Hiroto
Endo, Hisashi
Henry, Nicolas
Berney, Cedric
Mahe, Frederic
Poulain, Julie
Labadie, Karine
Beluche, Odette
El Hourany, Roy
Chaffron, Samuel
Wincker, Patrick
Nakamura, Ryosuke
Karp-Boss, Lee
Boss, Emmanuel
Bowler, Chris
de Vargas, Colomban
Tomii, Kentaro
Ogata, Hiroyuki
eng
18H02279/MEXT | Japan Society for the Promotion of Science (JSPS)/
19H05667/MEXT | Japan Society for the Promotion of Science (JSPS)/
JPMJSP2110/MEXT | Japan Science and Technology Agency (JST)/
ANR-10-INBS-09/Agence Nationale de la Recherche (French National Research Agency)/
101082021/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/
835067/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/
England
2023/09/23
ISME Commun. 2023 Sep 22;3(1):101. doi: 10.1038/s43705-023-00308-7.},
   abstract = {Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.},
   ISSN = {2730-6151 (Electronic)
2730-6151 (Print)
2730-6151 (Linking)},
   DOI = {10.1038/s43705-023-00308-7},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/37740029},
   year = {2023},
   type = {Journal Article}
}

{"_id":"ffnTc47K8Wuo89SPc","bibbaseid":"kaneko-endo-henry-berney-mahe-poulain-labadie-beluche-etal-predictingglobaldistributionsofeukaryoticplanktoncommunitiesfromsatellitedata-2023","author_short":["Kaneko, H.","Endo, H.","Henry, N.","Berney, C.","Mahe, F.","Poulain, J.","Labadie, K.","Beluche, O.","El Hourany, R.","Tara Oceans, C.","Chaffron, S.","Wincker, P.","Nakamura, R.","Karp-Boss, L.","Boss, E.","Bowler, C.","de Vargas, C.","Tomii, K.","Ogata, H."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Kaneko"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Endo"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Henry"],"firstnames":["N."],"suffixes":[]},{"propositions":[],"lastnames":["Berney"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Mahe"],"firstnames":["F."],"suffixes":[]},{"propositions":[],"lastnames":["Poulain"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Labadie"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Beluche"],"firstnames":["O."],"suffixes":[]},{"propositions":[],"lastnames":["El","Hourany"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Tara","Oceans"],"firstnames":["Coordinators"],"suffixes":[]},{"propositions":[],"lastnames":["Chaffron"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Wincker"],"firstnames":["P."],"suffixes":[]},{"propositions":[],"lastnames":["Nakamura"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Karp-Boss"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Boss"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Bowler"],"firstnames":["C."],"suffixes":[]},{"propositions":["de"],"lastnames":["Vargas"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Tomii"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Ogata"],"firstnames":["H."],"suffixes":[]}],"title":"Predicting global distributions of eukaryotic plankton communities from satellite data","journal":"ISME Commun","volume":"3","number":"1","pages":"101","note":"Kaneko, Hiroto Endo, Hisashi Henry, Nicolas Berney, Cedric Mahe, Frederic Poulain, Julie Labadie, Karine Beluche, Odette El Hourany, Roy Chaffron, Samuel Wincker, Patrick Nakamura, Ryosuke Karp-Boss, Lee Boss, Emmanuel Bowler, Chris de Vargas, Colomban Tomii, Kentaro Ogata, Hiroyuki eng 18H02279/MEXT | Japan Society for the Promotion of Science (JSPS)/ 19H05667/MEXT | Japan Society for the Promotion of Science (JSPS)/ JPMJSP2110/MEXT | Japan Science and Technology Agency (JST)/ ANR-10-INBS-09/Agence Nationale de la Recherche (French National Research Agency)/ 101082021/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ 835067/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ England 2023/09/23 ISME Commun. 2023 Sep 22;3(1):101. doi: 10.1038/s43705-023-00308-7.","abstract":"Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.","issn":"2730-6151 (Electronic) 2730-6151 (Print) 2730-6151 (Linking)","doi":"10.1038/s43705-023-00308-7","url":"https://www.ncbi.nlm.nih.gov/pubmed/37740029","year":"2023","bibtex":"@article{RN294,\r\n author = {Kaneko, H. and Endo, H. and Henry, N. and Berney, C. and Mahe, F. and Poulain, J. and Labadie, K. and Beluche, O. and El Hourany, R. and Tara Oceans, Coordinators and Chaffron, S. and Wincker, P. and Nakamura, R. and Karp-Boss, L. and Boss, E. and Bowler, C. and de Vargas, C. and Tomii, K. and Ogata, H.},\r\n title = {Predicting global distributions of eukaryotic plankton communities from satellite data},\r\n journal = {ISME Commun},\r\n volume = {3},\r\n number = {1},\r\n pages = {101},\r\n note = {Kaneko, Hiroto\r\nEndo, Hisashi\r\nHenry, Nicolas\r\nBerney, Cedric\r\nMahe, Frederic\r\nPoulain, Julie\r\nLabadie, Karine\r\nBeluche, Odette\r\nEl Hourany, Roy\r\nChaffron, Samuel\r\nWincker, Patrick\r\nNakamura, Ryosuke\r\nKarp-Boss, Lee\r\nBoss, Emmanuel\r\nBowler, Chris\r\nde Vargas, Colomban\r\nTomii, Kentaro\r\nOgata, Hiroyuki\r\neng\r\n18H02279/MEXT | Japan Society for the Promotion of Science (JSPS)/\r\n19H05667/MEXT | Japan Society for the Promotion of Science (JSPS)/\r\nJPMJSP2110/MEXT | Japan Science and Technology Agency (JST)/\r\nANR-10-INBS-09/Agence Nationale de la Recherche (French National Research Agency)/\r\n101082021/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/\r\n835067/EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/\r\nEngland\r\n2023/09/23\r\nISME Commun. 2023 Sep 22;3(1):101. doi: 10.1038/s43705-023-00308-7.},\r\n abstract = {Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.},\r\n ISSN = {2730-6151 (Electronic)\r\n2730-6151 (Print)\r\n2730-6151 (Linking)},\r\n DOI = {10.1038/s43705-023-00308-7},\r\n url = {https://www.ncbi.nlm.nih.gov/pubmed/37740029},\r\n year = {2023},\r\n type = {Journal Article}\r\n}\r\n\r\n","author_short":["Kaneko, H.","Endo, H.","Henry, N.","Berney, C.","Mahe, F.","Poulain, J.","Labadie, K.","Beluche, O.","El Hourany, R.","Tara Oceans, C.","Chaffron, S.","Wincker, P.","Nakamura, R.","Karp-Boss, L.","Boss, E.","Bowler, C.","de Vargas, C.","Tomii, K.","Ogata, H."],"key":"RN294","id":"RN294","bibbaseid":"kaneko-endo-henry-berney-mahe-poulain-labadie-beluche-etal-predictingglobaldistributionsofeukaryoticplanktoncommunitiesfromsatellitedata-2023","role":"author","urls":{"Paper":"https://www.ncbi.nlm.nih.gov/pubmed/37740029"},"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"https://bibbase.org/network/files/CzfdMm64N7sna2FRu","dataSources":["oN8Wd4zAfgwaCR7pT","Ly5oTfXjg2ogJZ3Mt","WBzsMjaYZGMDcZMoy","vAJa6ptXSf5sWDNtC","TkaCSd5Aj84jXPcpB"],"keywords":[],"search_terms":["predicting","global","distributions","eukaryotic","plankton","communities","satellite","data","kaneko","endo","henry","berney","mahe","poulain","labadie","beluche","el hourany","tara oceans","chaffron","wincker","nakamura","karp-boss","boss","bowler","de vargas","tomii","ogata"],"title":"Predicting global distributions of eukaryotic plankton communities from satellite data","year":2023,"downloads":1}